About Coinflow

Coinflow is the next-generation payment service provider revolutionizing global financial infrastructure with stablecoins, AI-driven fraud prevention, and instant settlement. Coinflow enables businesses to grow faster with instant settlement, fraud & chargeback indemnity, global pay-ins, multi-currency FX, and unified payouts. Founded in 2023, the company serves marketplaces, fintechs, remittance providers, gaming platforms, and ecommerce merchants worldwide.

Since our seed round in 2024, we’ve achieved 23x revenue growth and scaled to multi-billion-dollar annual transaction volume. In response to this growth, Coinflow announced a $25M Series A in October 2025—led by Pantera Capital, CMT Digital, Coinbase Ventures, Jump Crypto, and Reciprocal Ventures—accelerating our mission to power the world’s fastest-moving businesses with innovative, reliable global payments.

Coinflow is proudly headquartered in Chicago, IL. Learn more at coinflow.cash.

Role Overview

Coinflow is seeking a Machine Learning Lead to own the fraud and risk intelligence layer at the core of our platform.

This is a founding ML role. You'll lead our first dedicated ML team, building capabilities that combine our first-party transaction data with partner signals to optimize approval rates across payment methods and geographies, sharpen risk decisioning during merchant underwriting, and improve fraud detection across global payment methods. That means digging into large-scale transaction and behavioral data, shipping production fraud models, defining what good looks like, and continuously raising the bar on detection and precision as our volume and merchant base scale.

The ideal candidate has hands-on experience building fraud models on the acquiring side of payments and working alongside external fraud vendors to tackle card-present or card-not-present fraud, authorization decisioning, chargeback reduction, and related risk systems.

Key Responsibilities

Strengthen Coinflow's fraud detection and risk decisioning capabilities — feature engineering, model development, and production deployment

Own the full model lifecycle: experimentation, evaluation, monitoring, and iteration

Define and track core fraud and risk metrics — detection rate, false positive rate, chargeback rate, dispute win rate — and continuously improve them

Explore transaction and behavioral data to surface new fraud signals and emerging attack patterns

Partner with Engineering, Product, and Operations to embed fraud intelligence directly into payment flows and internal tooling

Integrate and orchestrate external fraud/risk partners, getting maximum value from their tooling

Establish the foundation for ML and data practices across the company

Help shape Coinflow's long-term fraud, risk, and ML roadmap

Required Qualifications

5+ years in machine learning, applied data science, or production ML roles

Demonstrated experience building fraud models in payments, with direct exposure to the acquiring side — acquirer, PSP, or payment facilitator

Proven track record taking ML projects from proof-of-concept to fully deployed, productionized systems

Deep familiarity with acquiring-side fraud dynamics: authorization fraud, card-not-present fraud, friendly fraud, chargeback patterns, and merchant risk

Strong foundation in ML, statistics, and feature engineering on high-volume financial data

Comfortable owning ambiguous problems end-to-end and creating structure where none exists

Strong collaborator across Engineering, Product, and Ops

Preferred Qualifications

Experience at an acquirer, ISO, PayFac, or payments infrastructure company

Experience developing, managing, and scaling MLOps pipelines and monitoring systems (retraining schedules, real-time performance metrics)

Experience scoping cloud compute requirements for scalable ML workloads

Familiarity with card network rules, dispute/chargeback workflows, and fraud liability frameworks

Experience as an early or sole ML hire at a startup

Exposure to real-time or near-real-time fraud scoring systems